Systems and methods for identifying a document with forged information

ABSTRACT

Methods and systems are provided for analyzing and assessing documents using a writing profile for documents, such as a payment instrument. A method may include providing a document to a computer system. In an embodiment, the method may further include comparing writing in information fields of the document to at least one forger writing profile representation. In some embodiments, at least one forger writing profile representation may be obtained from at least one information field of at least one document that includes forged information. In an embodiment, the document may be identified as a document including forged information from an approximate match of at least one forger writing profile representation with writing in the document.

PRIORITY CLAIM

[0001] This application is a continuing application of and claimspriority to U.S. patent application Ser. No. 10/389,265 entitled“Systems and Methods For Identifying a Document With Forged Information”filed by Houle, et al. on Mar. 14, 2003, which claims priority to U.S.Provisional Application No. 60/364,675 entitled “Systems and Methods forHandwriting Analysis in Documents,” filed Mar. 15, 2002.

BACKGROUND OF THE INVENTION

[0002] 1. Field of the Invention

[0003] The present invention generally relates to analyzing informationin documents such as payment instruments. Certain embodiments relate tocomputer-implemented systems and methods for analyzing and assessingdocuments.

[0004] 2. Description of the Related Art

[0005] Fraud related to forgery of documents, such as checks, hasincreased steadily worldwide over the past few years. For example, inEurope fraud has doubled in the past two years. This is a very difficultproblem mainly because of the wide range of techniques used to reroutemoney from an account to a fraudulent account. Fraud may be found in anydocument-based business where money transfers take place. There has beena significant amount of effort applied in developing technology, such assignature verification, for assessing forgeries in financial documents.

[0006] Many financial institutions, such as banks, are required to keepcopies of processed financial documents for a long period of time, forexample, months, and even years. Such institutions commonly employimage-based financial document systems that store images of processeddocuments in the form of images on a database on a computer system.

[0007] Databases including images depicting handwriting known to beauthentic are an important resource for methods and systems of assessingforgery. A handwriting sample of unknown validity, such as a signature,may be compared to images in such a database to determine if thehandwriting sample is a forgery. However, such a process may bedifficult and expensive if the database includes a very large amount ofimage data. In addition, many methods and systems for assessing forgeryin financial documents focus on assessing forgery in a limited portionof the document, for example, of a signature. Such methods and systemsmay lead to a large number of financial documents being incorrectlylabeled as containing forgeries, as well as failing to identify forgedcontents in non-signature portions of a document.

[0008] U.S. Pat. No. 6,157,731 Hu et al. discloses a signatureverification method and is incorporated by reference as if fully setforth herein. The method involves segmenting a smoothed and normalizedsignature and, for each segment, evaluating at least one local featureto obtain a feature value vector.

[0009] A method and system of recognizing handwritten words in scanneddocuments is disclosed in U.S. Pat. No. 6,108,444 to Syeda-Mahmood andis incorporated by reference as if fully set forth herein. A method ofdetecting and recognizing handwritten words is described. Theapplications described in the patent are directed to the use ofhandwriting recognition algorithms as part of keyword searches.

[0010] U.S. Pat. No. 6,084,985 to Dolfing et al. discloses a method foron-line handwriting recognition and is incorporated by reference as iffully set forth herein. The method employs feature vectors based onaggregated observations.

[0011] U.S. Pat. No. 5,995,953 to Rindtorff et al. discloses a method ofcomparing handwriting and signatures and is incorporated by reference asif fully set forth herein. The method relies on comparison of featuresof a signature rather than the images of signatures.

[0012] U.S. Pat. No. 5,909,500 to Moore discloses a method of signatureverification and is incorporated by reference as if fully set forthherein. The method is based on analysis of the environs attendant to thesignature string.

[0013] U.S. Pat. No. 5,710,916 to Barbara et al. discloses a method andapparatus for similarity matching of handwritten data objects and isincorporated by reference as if fully set forth herein.

[0014] A method of signature verification is disclosed in U.S. Pat. No.5,828,772 to Kashi et al. The method compares the numerical values ofparameters evaluated on a trial signature with stored reference dataderived from previously entered reference signatures.

[0015] U.S. Pat. No. 5,680,470 to Moussa et al. discloses a method ofautomated signature verification and is incorporated by reference as iffully set forth herein. In the method, a test signature, for example, asignature entered by an operator, may be preprocessed and examined fortest features. The test features may be compared against features of aset of template signatures, and verified in response to the presence orabsence of the test features in the template signatures.

[0016] U.S. Pat. No. 5,454,046 to Carman discloses a universalhandwriting recognition system and is incorporated by reference as iffully set forth herein. The system converts user-entered time orderedstroke sequences into computer readable text.

SUMMARY OF THE INVENTION

[0017] An embodiment of the present invention relates to acomputer-implemented method for analyzing and assessing fraud indocuments. Analysis and assessment of documents may use a profilecreated for authorized writers of a document.

[0018] In one embodiment, a method of generating a writing profile on acomputer system may include providing one or more documents to thecomputer system. In some embodiments, at least one of the documents mayinclude at least one information field. In other embodiments, at leastone of the documents may include at least two information fields. Themethod may further include determining at least one writing profilerepresentation for at least two of the information fields using writingfrom at least one of the information fields. Alternatively, the methodmay include determining at least one writing profile representation forat least one of the information fields using writing from at least twoof the information fields. In other embodiments, at least two writingprofile representations for at least one of the information fields maybe assessed using writing from at least one of the information fields.

[0019] In an embodiment, a method of generating a writing profile on acomputer system may further include providing one or more additionaldocuments to the computer system. At least one of the additionaldocuments may include at least one information field. In anotherembodiment, at least one of the additional documents may include atleast two information fields. The method may further include updating atleast one of the writing profile representations using at least one ofthe information fields of at least one of the additional documents.

[0020] In an embodiment, a method of assessing a document using acomputer system may include providing a document to the computer system.In some embodiments, the document may include at least one informationfield. Alternatively, the document may also include at least twoinformation fields. The method may further include comparing writing inat least two of the information fields of the document to at least onewriting profile representation. At least one writing profilerepresentation may be from at least one information field of at leastone other document. Alternatively, the method may include comparingwriting in at least one of the information fields of the document to atleast one writing profile representation. At least one writing profilerepresentation may be from at least two information fields of at leastone other document. In other embodiments, writing in at least one of theinformation fields of the document may be compared to at least twowriting profile representations. At least two writing profilerepresentations may be from at least one information field of at leastone other document.

[0021] In one embodiment, a method of assessing information in adocument using a computer system may include obtaining information onwriting in an information field of a document. The document may includeat least two information fields. The method may further includecomparing the obtained written information in the information field andwritten information in at least one other information field to at leastone writing profile representation. In another embodiment, the methodmay include comparing the obtained written information in theinformation field and written information in at least two otherinformation fields to at least one writing profile representation.Alternatively, the obtained written information in the information fieldand written information in at least one other information field may becompared to at least two writing profile representations from at leastone other document.

[0022] In some embodiments, at least one of the writing profilerepresentations may include written information from the informationfield and written information from at least one of the other informationfields. In other embodiments, at least one of the writing profilerepresentations may include written information from the informationfield and written information from at least two of the other informationfields from at least the one of the other documents. Alternatively, atleast two of the writing profile representations may include writteninformation from the information field and written information from atleast one of the other information fields from at least the one otherdocument.

[0023] In an embodiment, a method of identifying a document with forgedinformation using a computer system may include providing a document tothe computer system. The document may include at least one informationfield. Alternatively, the document may include at least two informationfields. The method may further include comparing writing in at least twoof the information fields of the document to at least one forger writingprofile representation. At least one forger writing profilerepresentation may be from at least one information field of at leastone document that includes forged information. In another embodiment,the method may include comparing writing in at least one of theinformation fields of the document to at least one forger writingprofile representation. At least one forger writing profile may be fromat least two information fields of at least one document that includesforger information. Alternatively, writing in at least one of theinformation fields of the document may be compared to at least twoforger writing profile representations. At least one forger writingprofile representation may be from at least one information field of atleast one document that includes forger information.

[0024] The method may additionally include identifying the document as adocument that includes forged information. The identification may bemade from an approximate match of at least one forger writing profilerepresentation with writing in the document.

[0025] In certain embodiments, a method of capturing written informationfrom an information field of a document using a computer system mayinclude providing a document to the computer system. The document mayinclude at least one information field. The method may further includeassessing whether writing in an information field approximately matchesa writing profile representation. The writing profile representation maybe from at least one information field from at least one other document.In an embodiment, at least one matching writing profile representationis associated with a corresponding text representation in a computerprocessable format in memory on the computer system. Additionally, themethod may include associating the information field with the textrepresentation corresponding to the matching writing profilerepresentation.

[0026] In an embodiment, a method of assessing a document using acomputer system may include providing a document to the computer system.In some embodiments, the document may include at least one informationfield. Alternatively, the document may include at least two informationfields. The method may further include comparing pre-printed informationin at least two of the information fields of the document to at leastone pre-printed profile representation. At least one pre-printed profilerepresentation may be from at least one information field of at leastone other document. Alternatively, the method may include comparingpre-printed text in at least one of the information fields of thedocument to at least one pre-printed profile representation. At leastone pre-printed profile representation may be from at least oneinformation field of at least one other document. In other embodiments,pre-printed information in at least one of the information fields of thedocument may be compared to at least two pre-printed profilerepresentations. At least two pre-printed profile representations may befrom at least one information field of at least one other document.

BRIEF DESCRIPTION OF THE DRAWINGS

[0027] A better understanding of the present invention may be obtainedwhen the following detailed description of preferred embodiments isconsidered in conjunction with the following drawings, in which:

[0028]FIG. 1 depicts an embodiment of a network diagram of a wide areanetwork suitable for implementing various embodiments;

[0029]FIG. 2 depicts an embodiment of a computer system suitable forimplementing various embodiments;

[0030]FIG. 3 illustrates an embodiment of a system and method foranalyzing documents;

[0031]FIG. 4 depicts an illustration of a check;

[0032]FIG. 5 depicts an illustration of a giro;

[0033]FIG. 6 depicts a flow chart of a method for assessing fraud indocuments;

[0034]FIG. 7 illustrates writing features included in mathematicalrepresentations of writing;

[0035]FIG. 8 illustrates writing features included in mathematicalrepresentations of writing;

[0036]FIG. 9 illustrates writing features included in mathematicalrepresentations of writing;

[0037]FIG. 10 illustrates legal amount entries in a legal amount field;

[0038]FIG. 11 depicts a flow chart of a method of generating a writingprofile;

[0039]FIG. 12 illustrates determining a handwriting profile fromhandwriting samples;

[0040]FIG. 13 illustrates dynamic variation of handwriting;

[0041]FIG. 14 depicts a flow chart of a method of generating a writingprofile from images in a computer database;

[0042]FIG. 15 depicts a flow chart of a method of generating a writingprofile from images presented for processing;

[0043]FIG. 16 depicts a flow chart of a method for assessing a document;

[0044]FIG. 17 depicts a flow chart of a method for assessing a document;

[0045]FIG. 18 depicts a flow chart of a method for assessing a document;

[0046]FIGS. 19 and 20 illustrate assessing fraud in the signature fieldof a giro;

[0047]FIG. 21 is an illustration of assessing fraud in a check;

[0048]FIG. 22 is an illustration of assessing fraud in a giro;

[0049]FIG. 23 is an illustration of assessing fraud in the city field ofa giro;

[0050]FIG. 24 depicts a flow chart of a method for assessing a document;

[0051]FIG. 25 illustrates converting a character in a handwriting imageto a mathematical representation;

[0052]FIG. 26 illustrates assessing fraud in a numeric field of apayment instrument;

[0053]FIG. 27 depicts a flow chart of a method for assessing a document;

[0054]FIG. 28 is an illustration of assessing fraud in a check;

[0055]FIG. 29 depicts a flow chart of a method for assessing a document;

[0056]FIG. 30 illustrates assessing fraud in a date field of a paymentinstrument;

[0057]FIG. 31 depicts a flow chart of a method for assessing a document;

[0058]FIG. 32 illustrates assessing fraud in a city field of a giro;

[0059]FIG. 33 is an illustration of assessing fraud in a check;

[0060]FIG. 34 depicts a flow chart of a method for assessing a document;

[0061]FIG. 35 depicts a flow chart of a method for assessing a document;

[0062]FIG. 36 depicts stock characteristics of a check;

[0063]FIG. 37 depicts a flow chart of a method for assessing a document;

[0064]FIG. 38 illustrates assessing fraud in a giro;

[0065]FIG. 39 depicts a flow chart of a method for assessing a document;

[0066]FIGS. 40a-d illustrate assessing fraud in a giro;

[0067]FIG. 41 depicts a flow chart of a method for assessing a document;

[0068]FIG. 42 depicts a flow chart of a method for capturing writteninformation from a document; and

[0069]FIG. 43 illustrates capturing written information from a document.

[0070] While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof are shown by way ofexample in the drawings and will herein be described in detail. Itshould be understood, however, that the drawings and detaileddescription thereto are not intended to limit the invention to theparticular form disclosed, but on the contrary, the intention is tocover all modifications, equivalents and alternatives falling within thespirit and scope of the present invention as defined by the appendedclaims.

DETAILED DESCRIPTION OF SEVERAL EMBODIMENTS

[0071]FIG. 1 illustrates a wide area network (“WAN”) according to oneembodiment. WAN 102 may be a network that spans a relatively largegeographical area. The Internet is an example of WAN 102. WAN 102typically includes a plurality of computer systems that may beinterconnected through one or more networks. Although one particularconfiguration is shown in FIG. 1, WAN 102 may include a variety ofheterogeneous computer systems and networks that may be interconnectedin a variety of ways and that may run a variety of softwareapplications.

[0072] One or more local area networks (“LANs”) 104 may be coupled toWAN 102. LAN 104 may be a network that spans a relatively small area.Typically, LAN 104 may be confined to a single building or group ofbuildings. Each node (i.e., individual computer system or device) on LAN104 may have its own CPU with which it may execute programs, and eachnode may also be able to access data and devices anywhere on LAN 104.LAN 104, thus, may allow many users to share devices (e.g., printers)and data stored on file servers. LAN 104 may be characterized by avariety of types of topology (i.e., the geometric arrangement of deviceson the network), of protocols (i.e., the rules and encodingspecifications for sending data, and whether the network uses apeer-to-peer or client/server architecture), and of media (e.g.,twisted-pair wire, coaxial cables, fiber optic cables, and/or radiowaves).

[0073] Each LAN 104 may include a plurality of interconnected computersystems and optionally one or more other devices such as one or moreworkstations 110 a, one or more personal computers 112 a, one or morelaptop or notebook computer systems 114, one or more server computersystems 116, and one or more network printers 118. As illustrated inFIG. 1, an example LAN 104 may include one of each computer systems 110a, 112 a, 114, and 116, and one printer 118. LAN 104 may be coupled toother computer systems and/or other devices and/or other LANs 104through WAN 102.

[0074] One or more mainframe computer systems 120 may be coupled to WAN102. As shown, mainframe 120 may be coupled to a storage device or fileserver 124 and mainframe terminals 122 a, 122 b, and 122 c. Mainframeterminals 122 a, 122 b, and 122 c may access data stored in the storagedevice or file server 124 coupled to or included in mainframe computersystem 120.

[0075] WAN 102 may also include computer systems connected to WAN 102individually and not through LAN 104 for purposes of example,workstation 110 b and personal computer 112 b. For example, WAN 102 mayinclude computer systems that may be geographically remote and connectedto each other through the Internet.

[0076]FIG. 2 illustrates an embodiment of computer system 150 that maybe suitable for implementing various embodiments of a system and methodfor analyzing and assessing documents. Each computer system 150typically includes components such as CPU 152 with an associated memorymedium such as floppy disks 160. The memory medium may store programinstructions for computer programs. The program instructions may beexecutable by CPU 152. Computer system 150 may further include a displaydevice such as monitor 154, an alphanumeric input device such askeyboard 156, and a directional input device such as mouse 158. Computersystem 150 may be operable to execute the computer programs to implementcomputer-implemented systems and methods for analyzing and assessingdocuments.

[0077] Computer system 150 may include a memory medium on which computerprograms according to various embodiments may be stored. The term“memory medium” is intended to include an installation medium, e.g., aCD-ROM or floppy disks 160, a computer system memory such as DRAM, SRAM,EDO RAM, Rambus RAM, etc., or a non-volatile memory such as a magneticmedia, e.g., a hard drive or optical storage. The memory medium may alsoinclude other types of memory or combinations thereof. In addition, thememory medium may be located in a first computer which executes theprograms or may be located in a second different computer which connectsto the first computer over a network. In the latter instance, the secondcomputer may provide the program instructions to the first computer forexecution. Also, computer system 150 may take various forms such as apersonal computer system, mainframe computer system, workstation,network appliance, Internet appliance, personal digital assistant(“PDA”), television system or other device. In general, the term“computer system” may refer to any device having a processor thatexecutes instructions from a memory medium.

[0078] The memory medium may store a software program or programsoperable to implement a method for analyzing and assessing documents.The software program(s) may be implemented in various ways, including,but not limited to, procedure-based techniques, component-basedtechniques, and/or object-oriented techniques, among others. Forexample, the software programs may be implemented using ActiveXcontrols, C++ objects, JavaBeans, Microsoft Foundation Classes (“MFC”),browser-based applications (e.g., Java applets), traditional programs,or other technologies or methodologies, as desired. A CPU such as hostCPU 152 executing code and data from the memory medium may include ameans for creating and executing the software program or programsaccording to the embodiments described herein.

[0079] Various embodiments may also include receiving or storinginstructions and/or data implemented in accordance with the foregoingdescription upon a carrier medium. Suitable carrier media may includestorage media or memory media such as magnetic or optical media, e.g.,disk or CD-ROM, as well as signals such as electrical, electromagnetic,or digital signals, may be conveyed via a communication medium such asnetworks 102 and/or 104 and/or a wireless link.

[0080] The systems and methods disclosed herein for analyzing andassessing documents may be applied to various kinds of documents thatinclude handwriting and other machine-printed information. Documents maybe analyzed and assessed for fraud or forgery using a profile createdfor one or more authorized writers of a document. Writers may includeindividuals, entities, and/or representatives of entities. Writers mayalso include machines or devices that print writing for individuals orentities. The profile may contain writing characteristics for one ormore authorized writers. As used herein, “writing” may refer to, but isnot limited to characters and symbols formed by an individual with aninstrument (e.g., pen, pencil, stylus, rubber stamp, etc.) and/or formedby a machine (e.g., printer, typewriter, etc). As used herein,“handwriting” may refer to writing done by an individual with a writingimplement, in particular, the form of writing peculiar to a particularperson. As used herein, “machine-printed writing” may refer to writingformed by a machine. Documents may include, but are not limited to,payment instruments, receipts, securities documents, invoices, accountapplications, leases, contracts, credit card receipts and slips, loanapplications, credit cards, debit cards, school applications, governmentdocuments such as social security cards or driver licenses, and legaldocuments such as wills or divorce decrees. As used herein, “forgery”refers to falsely and fraudulently making or altering a document. Adocument may be forged with handwriting, a machine, and/or by othermeans. A forger may make or create an entire document or alter only aportion of a document. For example, a forger may obtain a check of anaccount owner containing no entries and enter information necessary toobtain a payment. Alternatively, a forger may obtain a check completewith entries of an account owner and alter one or more portions of thecheck.

[0081] For example, payment instruments may include various types ofcommercial paper such as a draft. As used herein, a “draft” is an orderto pay. Generally, a draft involves three parties. One party, the“drawer,” orders another party, the drawee (often a bank), to pay moneyto a third party, the “payee,” or to a bearer of the draft. A “check” isany draft drawn on a bank and payable on demand. Alternatively, apayment instrument may include a “giro.” A “giro” is a check-likepayment instrument commonly used to make payments in many Europeancountries.

[0082] In one embodiment, a document may include variable writteninformation and stock characteristics. Stock characteristics refer topre-printed information that tends not to vary on a particular set ofdocuments. A set of payment instruments for a payment instrument accountmay include one or more stock characteristics. For example, stockcharacteristics or pre-printed information may include machine-printedtext blocks, graphics elements (e.g., bank logo), and the relativepositions and/or locations of other stock characteristics.Machine-printed text blocks may include, for example, the name andaddress of one or more account owners and account numbers.Alternatively, variable written information or writing refers to writingthat tends to vary on a particular set of documents. The content ofvariable written information may depend on a particular purpose ortransaction. For example, for a payment instrument, variable writteninformation may include a payee, courtesy amount, date, etc.

[0083] Furthermore, a document may include one or more informationfields. In one embodiment, an “information field” may be a portion of adocument for entering variable written information. For example, the oneor more account owners of a checking account are a set of writers orindividuals that may enter written information in various portions of acheck. For instance, an account owner may write his or her signature inthe signature field of a check corresponding to the account of theaccount owner. As used herein, an “account” refers to a formal businessarrangement providing for regular dealings or services, such as banking,and involving the establishment and maintenance of an account. Writteninformation may be entered into information fields of a check by amachine, such as a printer. In some embodiments, an “information field”may refer to pre-printed information on a document, or a document stockcharacteristic, such as graphic elements or machine-printed text.

[0084]FIG. 3 illustrates an embodiment of a system and method foranalyzing and assessing documents. Document image archive 210 mayinclude an archive of images of documents that may include variablewritten information and/or pre-printed information. The document imagesmay be created from valid processed documents that include valid writteninformation corresponding to known individuals or writers. The documentimages may also be created from unprocessed and/or forged documents. Asused herein, an “image” is a representation of a graphics image incomputer memory. The image may be composed of rows and columns of dots.The value of each dot, e.g., whether it is filled in or not, is storedusing one or more bits of data. A “bit,” short for binary digit, is thesmallest unit of information on a computer system.

[0085] In one embodiment, document image archive 210 may include apayment instrument archive that includes images of valid processedpayment instruments. In some embodiments, the images in the archive mayinclude images of several types of documents corresponding to knownindividuals or entities. For example, the archive may include images ofchecks and images of credit card receipts corresponding to a particularindividual or individuals or entity. The particular individual orindividuals or entity may be authorized writers. “Authorized writers”generally refer to writers permitted and/or with the legal right to makeentries on a document, such as one or more account owners of a paymentinstrument account.

[0086] For example, a payment instrument archive may be created andstored by CheckVision software from Computer Sciences Corporation of ElSegundo, Calif.. A camera may be used to capture digital images ofpayment instruments. For example, a bank may capture digital images ofpayment instruments presented for payment. Digital images of paymentinstruments may be archived for analysis. In one embodiment, the imagesmay be transferred to archive 210 via the Internet. A database of imagesof any type of document including variable handwritten information,variable machine-printed information, and/or pre-printed information maybe created and stored on a memory medium.

[0087] As shown by data flow 218, document images from archive 210 maybe provided to document analyzer 214. Document analyzer 214 may create aprofile that corresponds to the writing of one or more individuals or anentity. The profile may also include pre-pre-printed information. Theprofile may be created from digital images of previously captureddocuments in the archive. In one embodiment, document analyzer 214 maybe a payment instrument analyzer that creates a payment instrumentaccount profile for an account from images of valid processed paymentinstruments of an account.

[0088] Document analyzer 214 may extract information from one or moreimages of documents of writers to create the profile. The profileinformation may include writing characteristics and patterns, datacontent, semantic patterns, and document layout that uniquelycharacterize the writers and the document. In one embodiment, thedocument profile may include profile information from more than one typeof document corresponding to known writers. As shown by data flow 220, aprofile may be stored in a profile database 212. In one embodiment,profile database 212 may be a payment instrument profile database. Incertain embodiments, the database may be stored in memory on a computersystem. Alternatively the database may be stored in memory on varioustypes of portable memory media not coupled with a computer system. Forexample, a memory medium may include a computer chip or magnetic strip.The computer chip or magnetic strip may be coupled with a card (e.g., acredit card, debit card, identification card, etc).

[0089] In one embodiment, document 213 may be provided to documentanalyzer 214, as shown by data flow 230. Document 213 may include one ormore information fields that include written and/or pre-printedinformation. Written information in the one or more information fieldsmay be asserted to have been entered by particular writers. Document 213may be, for example, an image of a payment instrument that waspreviously presented for payment to a bank. The writers may include oneor more account owners. Alternatively, document 213 may be a bankaccount application written by an applicant. The writers asserted tohave entered written information on document 213 may correspond to awriting profile that is stored on profile database 212. For example,document 213 may be a payment instrument that corresponds to a paymentinstrument account profile. Document analyzer 214 may perform one ormore analyses or tests for assessing fraud on document 213 using aprofile from the profile database 212, as shown by data flow 222. Adocument may be fraudulent if it has been altered, written, or createdby an individual other than one of the authorized writers for adocument, such as one or more payment instrument account owners. Anindividual who fraudulently writes, alters, or creates a document may bereferred to as a forger of a document. For example, a “forger” of apayment instrument may be an individual who alters or writes a paymentinstrument of an account not owned by the forger without the permissionof one or more of the account owners. In addition, a forger may be anindividual who signs a credit card slip corresponding to a credit cardaccount not held by the individual.

[0090] The results of the tests or analyses on document 213 may beprovided to a fraud detector 216, as indicated by data flow 224. Frauddetector 216 may assess from the tests or analyses whether document 213is potentially fraudulent. In one embodiment, if document 213 isassessed not to be a forgery, the computer system may notify a documentprocessing system 217 that the document is valid, as indicated by dataflow 226. Alternatively, fraud detector 216 may assess that document 213is potentially fraudulent. In this case, document 213 may be submitted,as indicated by data flow 228, for further review 219. The result ofdocument review 219 may be sent to document processing 217. For example,a reviewer may determine that a payment instrument is fraudulent andinstruct the bank not to make payment. The determination of whether thepayment instrument is fraudulent may involve further research. Forexample, a reviewer may contact the account owner corresponding to thepayment instrument. Rejected payment instruments may be returned todepositors.

[0091] In an alternative embodiment, document analyzer 214 may extractinformation from document 213 for purposes other than assessing fraud.For example, text from document may be recognized and extracted to savelabor that would be expended in keying in the text. In addition,information may be extracted from document 213 and stored in profiledatabase 212.

[0092] Furthermore, the information in the profile may be used for datamining. Data mining refers to the process of looking for hidden patternsin a group of data. Data mining may be used to find correlations betweeninformation fields to predict the content of, for example, a paymentinstrument presented for payment. For instance, data mining may be ableto predict an entry in one information field of a document based on theentry in another.

[0093] In one embodiment, a profile may include specific informationrelating to one or more information fields of a document. The specificinformation corresponding to each information field is generally enteredin writing by the writer of the document. Detection of handwriting in aninformation field of a document belonging to someone other than anauthorized writer is evidence of potential fraud. For example,handwriting in any information field of a payment instrument belongingto someone other than one of the account owners is evidence of fraud.

[0094]FIG. 4 depicts an illustration of a check that includeshandwritten information in the information fields of the check. Check264 includes payee field 266, date field 272, courtesy amount field 274,legal amount field 268, memo field 270, and signature field 276. Payeefield 266 generally includes the name of an individual or entity. Datefield 272 may include a date after which the check may be paid in termsof a month, day, and year. Courtesy amount field 274 may include theamount, for example, in dollars, in numeric form for which the check iswritten. Legal amount field 268 may include the amount in dollars inalphanumeric form for which the check is written. Memo field 270 mayinclude any information a writer of the check may desire to enter. Awriter may enter information in the memo field relating to the purposeof the payment, for example, “June Rent.” A writer may also enter anaccount number that corresponds to an account the writer has with apayee. For example, a writer may enter a writer's account number with autility company or a writer's credit card account number. Signaturefield 276 includes the handwritten signature of one of the owners of theaccount. As used herein, a “signature” may be defined as the name of aperson written with the person's own hand. Label 265 refers to the stockcharacteristics and/or pre-printed information of the check.

[0095]FIG. 5 depicts an illustration of a giro that includes handwritteninformation in information fields of the giro. Giro 278 includes debitaccount fields 280, amount field 282, description field 284, creditaccount field 286, name field 288, city field 290, and signature field292. Giro 278 also includes text 294. Debit account field 280 includesthe number of an account to be debited or charged against to pay theamount for which the giro is written. Amount field 282 may include theamount, for example in euros, in numeric form for which the giro iswritten. Description field 284, like the memo field, may include anyinformation a writer of the giro may desire to enter. Credit accountfield 286 includes the number of an account to be credited in the amountfor which the giro is written. Name field 288 includes the payee of thegiro. City field 290 includes the name of the city where the giro creditrecipient's bank is located. Signature field 292 includes a signature ofone of the giro account owners.

[0096] In one embodiment, a writing profile, such as a paymentinstrument account profile, may include profiles for one or more of theinformation fields in a document. The profile of the information fieldsmay include writing characteristics and patterns, data content, and/orsemantic patterns that uniquely characterize the writing entered intoinformation fields by particular writers. An information field of adocument, such as a payment instrument, may include one or more entrytypes that the writer of the document may enter in the informationfield. An entry type refers, for example, to a specific name or numberthat one or more owners of an account enter in a field. For instance,entry types of a payee field correspond to payee names to which accountowners write checks.

[0097] In an embodiment, a writing profile for a document, such as apayment instrument account profile, of an entry type of an informationfield may include one or more representations of the entry type. The oneor more representations may be referred to as writing profilerepresentations. Writing profile representations may include handwritingprofile representations and machine-printed profile representations. Anembodiment of a method of generating a payment instrument accountprofile for an account may include providing one or more paymentinstruments written by one or more account owners. At least one of thepayment instruments may include at least one information field. In oneembodiment, images of writing in at least one information field may beobtained. The payment instrument images may be obtained from the imagearchive discussed in FIG. 3. The method may further include determiningat least one profile representation of from at least one of theinformation fields.

[0098] At least one of the writing profile representations maycorrespond to at least one entry type of at least one of the informationfields. At least one variant of the written entry type of an informationfield may be included in the handwriting profile representations.

[0099] A “variant” refers to a distinct written sample of a type ofwritten information such as a character or set of characters. A type ofwritten information may be, for example, a letter of an alphabet or asignature. Generally, writing of an writer, such as an individual,includes writing characteristics and patterns, data content, and/orsemantic patterns that are unique to the writer. A single sample of anindividual's handwriting, for example, may not include all the uniqueproperties of the handwriting of an individual. A single variantincludes at least some of such properties. Variants of a particular typeof written information, such as a signature, may include a majority ofthe writing characteristics and patterns, data content, and/or semanticpatterns that are unique to the individual. For example, an individualmay consistently include a set of strokes in his or her signature.However, the individual may not include all such strokes in everysignature sample.

[0100] Furthermore, a method of assessing fraud in a document, such as apayment instrument, may include performing one or more fraud tests. Adocument may be fraudulent if at least some of the writing on thedocument was not entered by authorized writers permitted to make entrieson a document, such as one or more account owners of a paymentinstrument account. A document may also be fraudulent if at least someof the pre-printed information on the document does not approximatelymatch the pre-printed information in a pre-printed information profile.Fraud tests may include analyses of writing characteristics andpatterns, data content, and/or semantic patterns of entries inindividual information fields and between information fields of adocument such as a payment instrument. FIG. 6 depicts a flow chart of amethod for assessing fraud in a document. Assessing fraud in a documentmay include providing a document to computer system, as shown at step500. Fraud tests may then be performed, as indicated at step 502, on oneor more of the information fields of the document. At step 504, themethod may include assessing whether the payment instrument ispotentially fraudulent based on the results of the one or more fraudtests.

[0101] In an embodiment, a fraud test may include an assessment ofwhether information in an information field of the documentapproximately matches a writing profile or pre-pre-printed informationprofile (or payment instrument account profile) of the informationfield. Failure of information in an information field to approximatelymatch a writing profile may be evidence that the information was notmade by at least one of the authorized writers permitted to make entrieson the document (such as an account owner of a payment instrumentaccount). Matching information in an information field with a profilemay refer to comparing the information as a whole to the profile.Matching an information may also refer to comparing discrete elements orcharacteristics of the information to the profile. Therefore, matchingmay include a subset of several fraud tests. In one embodiment, a fraudtest may include analyzing variations among discrete elements ininformation in an information field. Similarly, a fraud test may includeanalyzing a comparison of information in different information fields.Another fraud test may include analyzing correlations of information indifferent information fields. In addition, a fraud test may includeassessing whether information in an information field approximatelymatches a lexicon associated with the information field.

[0102] The result of the fraud tests may provide evidence that adocument, such as a payment instrument, is potentially fraudulent. Thestrength of the indication of fraud may be different for each fraudtest. In one embodiment, one or more of the fraud tests may be assigneda fraud weight, such that the fraud weight corresponds to the strengthof the indication of fraud in the payment instrument. An assessment ofwhether a document, such as a payment instrument, is fraudulent may bebased on one or more of the fraud tests and the corresponding fraudweights. The assessment may be made in the fraud detector depicted inFIG. 3.

[0103] In an embodiment, when a fraud test indicates fraud, the computersystem may generate a flag indicating that the document is potentiallyfraudulent. The flag may include a fraud weight that corresponds to astrength of the indication of fraud of the fraud test. The fraud weightof a fraud test may depend on a number of factors. For example, fraudtests involving features that are consistently present in certaindocuments, such as payment instruments of an account, may receive agreater weight than fraud tests involving features present lessfrequently. For example, variations in writing features that are alwayspresent may receive a higher weight than variations in features that areinfrequently present. In addition, fraud tests involving fields wherefraud is frequently perpetrated in payment instruments, such as acourtesy amount field and a legal amount field, may receive a higherweight than fraud tests relating to other information fields. TABLE 1Summary of Content in a Document Profile and Corresponding AnalysisTechniques. Profile Component Content Analysis Technique InformationField Mathematical representations for Information Field Content Shapevariants of types of written Content Shape Analysis informationInformation Field Images for variants of types of written InformationField Content Image information Content Image Analysis Digit/AlphaMathematical representations for Digit/Alpha Analysis variants of aletter type and a numeral type Symbology Mathematical representationsfor Symbology Analysis variants of a symbol type Images for variants ofa symbol type Syntax Pattern Elements and ordering of elements in SyntaxPattern Analysis specific information fields Lexicon List of the namesthat have previously Lexicon Analysis been recognized on documentsassociated with a set of individuals and/or accounts Document Stock orRepresentation of the nature and Document Analysis Pre-printed locationof the graphic elements and Information machine-printed text that appearon a document associated with a set of individuals and/or accountsMathematical representations for variants of types of pre-printedinformation, including font type, information Images for variants oftypes of machine-printed information, including font type InformationField Table listing the cross field Cross Field Matching CrossCorrelation relationships of interest in a document Analysis associatedwith a set of individuals

[0104] Table 1 provides a summary of components of content (i.e.,variable writing and pre-printed information) in a document profile,such as a payment instrument account profile, and analysis techniquesaccording to one embodiment. The analysis techniques may be applied tothe corresponding profile contents to assess fraud in a document such asa payment instrument. The information field content shape profile mayinclude mathematical representations for variants of types of writteninformation. The information field content image profile may includeimages for the variants of types of written information. The writingprofiles may include representations of at least one font style ofmachine-printed writing. The digit/alpha profile may include bothmathematical representations and images for the variants of characterssuch as letters and numerals. The symbology profile may includemathematical representations and images of variants of symbols thatappear in the information field of a document such as a paymentinstrument (e.g., a ‘+’ in the legal amount field). The syntax patternprofile may include a list with elements and an order of the elements inspecific information fields. For example, a syntax pattern profile mayinclude the variants of the form of a month, day, year, and punctuationand the order of the month, day, and year and punctuation in the datefield. In addition, the lexicon profile may include a list of names thathave previously been recognized for an account in a particularinformation field, such as payee names in a payee field. The documentstock or pre-printed profile may include representations of pre-printedinformation such as graphic elements (e.g., bank logos) andmachine-printed text (e.g., name and address of account owners) thatappear on a document, such as a payment instrument. The document stockprofile may also include mathematical representations and/or images ofmachine-printed text. The information field cross correlation profilemay include a list of cross field relationships that may occur with aparticular frequency in a document associated with particular writers,such as in a payment instrument of an account. For example cross fieldrelationships may include: account number in a memo field to payee name,payee name to legal and courtesy amount, and identity of check writerfrom the signature field to syntax patterns and symbology in otherfields.

[0105] Handwriting and/or writing may include, but is not limited to amathematical representation and/or an image. Handwriting and/or writingmay also include, but is not limited to at least one type of handwrittenand/or written information such as a word type and/or character type.Handwriting and/or writing may further include, but is not limited to aglobal feature of handwriting and/or writing, a local feature ofhandwriting and/or writing, a syntax pattern, and/or a lexicon name foran information field. A handwriting and/or writing profilerepresentation may include, but is not limited to at least one of thetypes of handwriting and/or writing profiles described in Table 1. TABLE2 Summary of Content Analysis for Payment Instrument. Information FieldContent Analysis Pre-printed Matching of all preprinted informationincluding information machine-printed text, logos, line and othergraphic elements Font Matching Courtesy Amount Individual characteranalysis Character patterns surrounding the courtesy amount Symbologyused in writing the cents amount Legal Line - Dollar Global handwritingfeatures Amount Word matching Individual character analysis Symbologyconnecting the dollar and cent content Character patterns surroundingthe legal amount Legal Line - Cents Punctuation Amount Individual digitsPayee Global writing features Words Individual characters Lexiconmatching to generic lists of payees (e.g., common payees) Lexiconmatching to suspicious payees (payee names frequently involved intransactions with high fraud risk) Matching to ASCII list of payeescommon for the account Matching to handwriting of payees common for theaccount Signature Global writing characteristics Word matching MemoGlobal writing characteristics Individual character analysis DatePatterns Individual character analysis Endorsement Matching ofendorsement to payee

[0106] Table 2 describes a summary of various embodiments of analysisfor information fields of a payment instrument. The profiles describedin Table 1 may be applied to assess a payment instrument using theanalysis summarized in Table 2.

[0107] Profile representations of variable writing and pre-printedinformation (e.g., machine-printed text) may be stored in memory on acomputer system in terms of mathematical representations. In anembodiment, a writing profile, such as a payment instrument accountprofile, may include one or more mathematical representations ofvariable writing and/or pre-printed information. The mathematicalrepresentations may include one or more variants of an entry type of aninformation field. As noted above, a variant refers to a distinctversion of a type of written information. For example, the appearance ofa handwritten signature of an individual, such as an account owner, maytend to vary, even within a short time period. The account owner mayhave several distinct versions of his or her signature. The one or moremathematical representations in a signature profile for an account ownercorrespond to one or more of the variants of the signature. In otherembodiments, a writing profile, such as a payment instrument accountprofile, in memory on a computer system may include one or more images.In a similar manner, one or more of the images may include images of oneor more variants of an entry type of an information field.

[0108] In one embodiment, mathematical representations of writing may beexpressed as feature vectors. For example, U.S. Pat. Nos. 6,157,731 toHu et al., U.S. Pat. No. 6,084,985 to Dolfing et al., U.S. Pat. No.5,995,953 to Rindtorff et al., U.S. Pat. No. 5,828,772 to Kashi et al.,and U.S. Pat. No. 5,680,470 to Moussa et al., which are incorporated byreference as if fully set forth herein, disclose the use of featurevectors to represent handwriting. Feature vectors are vectors that mayinclude one or more writing features that characterize writing aselements of the vectors. For example, features included in a featurevector may represent the strokes that make up written information. Sincewriting has a local character and a global character, both localfeatures and global features may characterize writing. Global featuresdescribe general characteristics of writing. Consequently, globalfeatures may be discernible in all information fields of a document suchas a payment instrument. Global features in a profile for an informationfield may be the result of combining global features from writteninformation in several information fields. Global features may includeglobal slant, tangent entropy, global thickness of stroke (penthickness), and curvature entropy.

[0109] “Entropy” refers to a way of measuring information content.Something that is very predictable has an entropy of zero, whilesomething having little or no predictability, has maximum entropy. Themore predictable writing appears, the lower its entropy. Handwritingstrokes may be parallel or may have several directions. Handwriting thatis predictable may be composed of strokes in relatively regularpatterns. On the other hand, a signature with many vertical andhorizontal strokes may have higher entropy.

[0110] Curvature versus tangent entropy refer to a measure of changes incurves and stroke directions, respectively. The style of writers may beclassified according to their entropies. The entropy, H(i), of stroke imay be calculated by H(i)=−p_(i) log (p_(i)), where p_(i) is theprobability of stroke i.

[0111] Local features refer to characteristics specific to a particularwriting sample. Local features may include local slant, leading andtrailing tail shape and direction, topology of a digit, strokedistribution (e.g., proximity of strokes, height, width), localthickness of stroke (to evaluate shakiness in handwriting), alpha versusnumeric patterns (e.g., date), punctuation, symbols, ‘00’ shape inamounts, and ‘xx/100’ structures in the legal amount.

[0112] Furthermore, topological vectors may represent the relationshipbetween strokes. The topological vectors may include information aboutthe location of strokes with respect to one another, for example,tangent and direction vectors.

[0113] In an embodiment, one or more of the variants of types of writteninformation of an information field of a writing profile may include amajority of the features that characterize the writing of authorizedwriters, such as one or more account owners. Stable features refer tofeatures that tend to appear consistently in writing in differentsamples of a writer. Stable features may tend to be more significant infraud assessment and recognition than weak features. Weak features referto features that tend not to appear consistently in writing samples.Furthermore, writing in the information fields of a document, such as acheck or giro, may be converted into a mathematical representation.

[0114]FIGS. 7, 8, 9, and 10 illustrate features that may be included inmathematical representations of information or entries in informationfields of a payment instrument. FIG. 7 represents nine variations of asignature entry in a signature field. Although some variation exists inthe signature entries, bottom left stroke 364 and top right stroke 366are consistently present. Such features may be considered to be stablefeatures. FIG. 8 represents entries from a payee field. Severalcharacteristics are consistent in each of entries 324-330. For example,the characters tend to be upright with block letters. Also, thecharacter shapes, such as that of the ‘C.’, are consistent. FIG. 9represents entries from a courtesy amount field. The courtesy amountentries also exhibit several consistent features. These include thecents ‘00’ and non ‘00’ features, as shown by entries 332 and 334. Digitshapes are also consistent, for example, the ‘4’s, as shown by entries333 and 334 and the ‘2’s, as shown by entries 335 and 336. FIG. 10represents legal amount entries in a legal amount field. The legalamount entries include dollar amount 348, symbol (‘+’) 350, and symbol(‘xx/100’) 349. The dollar amount and symbols are consistent among thevarious entries.

[0115] An embodiment of a method of generating a writing profile forinformation fields of a document is depicted by a flow chart in FIG. 11.In an embodiment, one or more of the documents may be a paymentinstrument. The information fields may include handwriting of one ormore account owners of a payment instrument account. The method mayinclude providing one or more documents to the computer system, as shownin step 526 of FIG. 11. In one embodiment, at least one of the documentsmay include at least one information field. In other embodiments, atleast one of the documents may include at least two information fields.One or more of the documents may be provided by accessing at least oneimage from a database in memory on a computer system. At least one ofthe documents may be a valid document. In certain embodiments, themethod may include obtaining images of writing in the informationfields. The method may further include determining at least one writingprofile representation for at least two information fields, as shown instep 528. Determining at least one writing profile representation mayuse writing from at least one of the information fields of thedocuments. At least one of the writing profile representations may bestored on a memory medium on a computer system.

[0116] In an embodiment, at least one writing profile representation maybe assessed for at least two of the information fields using writingfrom at least one of the information fields. Alternatively, at least twowriting profile representations may be assessed. In some embodiments,writing may be used from at least two information fields.

[0117] In other embodiments, at least two writing profilerepresentations may be assessed for at least one of the informationfields using writing from at least one of the information fields.Alternatively, at least two writing profile representations may beassessed for at least one of the information fields. In someembodiments, writing may be used from at least two information fields.

[0118] In some embodiments, at least one writing profile representationmay be assessed for at least one of the information fields usinghandwriting from at least two of the information fields. Alternatively,at least two writing profile representations may be assessed. In anotherembodiment, at least one writing profile representation may be assessedfor at least two of the information fields. Some embodiments may furtherinclude determining at least two writing profile representations for atleast two of the information fields using writing from at least twoinformation fields.

[0119] The method may further include determining mathematicalrepresentations of writing in one or more of the information fields. Insome embodiments, determining mathematical representations of writingmay involve converting images of writing in at least one of theinformation fields to mathematical representations.

[0120] At least one of the writing profile representations may includemathematical representations of the writing in the information fields.In an embodiment, determining writing profile representations mayinvolve determining variants of the mathematical representations ofwriting. In an embodiment, at least one writing profile representationmay include at least one writing variation (variant) of an example of atleast one type of written information. For example, profilerepresentations, belonging to the Digit/Alpha profile described in Table1, may include variants of the handwritten letter “a.” The writingprofile representations may also include variants of one or more entrytypes in the one or more information fields. In an embodiment, at leastone of the writing profile representations may be an image. In otherembodiments, at least one of the writing profile representations may bea mathematical representation.

[0121] The conversion of an image to a mathematical representationexpressed as feature vectors is disclosed in U.S. Pat. Nos. 6,157,731 toHu et al., U.S. Pat. No. 6,084,985 to Dolfing et al., U.S. Pat. No.5,995,953 to Rindtorff et al., U.S. Pat. No. 5,828,772 to Kashi et al.,and U.S. Pat. No. 5,680,470 to Moussa et al., which are incorporated byreference as if fully set forth herein. For example, one method mayinclude first converting an image to a runlen image. A “runlen image”refers to a raster scan image that is represented by black (where thetext is) and white runs (where the background is). A runlen image has Nlines (in a raster scan) and each line is represented by a variablenumber of runs (white-black-white-black-..). A run may be represented bya start point and its length. The runlen image may then be converted toa Freeman chain code. A “Freeman chain code” is an image representationthat has an 8-direction code that provides a contour of an object in animage, for example, a letter. The Freeman chain code may then beconverted to a tangent, which may be converted to curvature. Thecurvature may then be converted to a handwriting or writing stroke.

[0122] In an embodiment, one or more variants among the mathematicalrepresentations may be assessed using a clustering algorithm. Clusteringalgorithms are methods of grouping large sets of data into clusters ofsmaller sets of similar data. The goal of a clustering algorithm is toreduce an amount of data by categorizing or grouping similar data itemstogether into a “cluster.” A clustering algorithm finds natural groupsof components (or data) based on some similarity. In particular, aclustering algorithm may determine groups of mathematicalrepresentations based on similarity of writing features. The clusteringalgorithms that assess the variants of a type of written information mayuse curvature and tangent profile matching (entropy measure), dynamicwarping/matching, and K-nearest neighbor techniques.

[0123]FIG. 12 is an illustration of determining variants for ahandwriting profile from handwriting samples. Handwriting samples 312depict six different samples of a signature from the signature field ofa check. The signature samples are very consistent with little variationfrom one sample to another. Therefore, only one variant may be assessedto characterize the signature of the account holder (e.g., variant 318).Handwriting samples 314 depict seven samples from the city field of agiro. There is also little variation in the samples of the city name. Inthis case, variant 320 may be assessed to characterize the city name. Inaddition, handwriting samples 315 depict samples of the payee line of acheck. A small amount of variation is exhibited between the samples.Variant 317 may be assessed to characterize the samples. Handwritingsamples 316 depict seven samples of a legal amount from a check. In thiscase, there are three entry types with respect to the dollar amount:four samples with “one hundred dollars” and one sample each for “two,”“three,” and “four hundred dollars.” Only one variant of the “onehundred dollars” samples may be necessary.

[0124] As described herein, a writing profile may be assessed from morethan one sample of writing, as shown in FIG. 12. It is advantageous tobase a writing profile on more than one sample due to variations inwriting of the authorized writers. A greater variation in writing of theauthorized writers may require a greater number of samples tocharacterize the writing with a writing profile.

[0125] Variation in handwriting may be both inherent and dynamic.Dynamic variation refers to the change in an individual's handwritingover time, for example, over months and years. For example, thevariation of a date from a date field and a signature from checkswritten over a period of months is illustrated in FIG. 13. Samples 298,300, 302, 304, and 306 represent a variation from May to September ofthe year 2001. In this case, the dynamic variation for both the date andthe signature is apparent. The appearance of the signature variesbetween May and September. In addition, there is a change in the syntaxof the date field from an alphabetic month to a numeric month betweenMay and September.

[0126] Inherent variations refer to variations in handwriting that areindependent of time or that may occur over a short period of time, forexample, over days. The source of inherent variations may beinconsistent handwriting of an individual. For instance, an individualmay consistently write a signature two or more ways.

[0127] In one embodiment, a method of generating a handwriting profile,such as a payment instrument account profile that takes into accountinherent variations is depicted in FIG. 14. In step 506, one or moredocuments may be provided to a computer system. The documents may beprovided from a database corresponding to a document archive shown inFIG. 3. The documents may be valid documents submitted by authorizedwriters. The documents may take into account variations in thehandwriting of the authorized writers only up to the date of the latestsubmitted document in the database. At least one handwriting profilerepresentation may be assessed from one or more of the documents, asshown in step 508. The handwriting profile representations may be, forexample, images and/or mathematical representations. One or more of thehandwriting profile representations may then be stored in memory on acomputer system, as shown in step 510. Alternatively, at least one ofthe handwriting profile representations may be stored on various typesof portable memory media not coupled with a computer system. Forexample, the handwriting profile representations may be stored in ahandwriting profile database depicted in FIG. 3.

[0128] In an embodiment, a method of generating a handwriting profile ona computer system that accounts for dynamic variations in handwriting isdepicted in FIG. 15. As shown in step 517, the method may includeproviding one or more documents to the computer system. At least one ofthe documents may include at least one information field. In someembodiments, the documents may include at least two information fields.At least one of the documents may be a valid document, such as avalidated payment instrument. In an embodiment, the one or moredocuments may be provided to the computer system by providing images ofthe document to the computer system. At least one handwriting profilerepresentation may be assessed for at least two of the informationfields using the handwriting from at least one of the informationfields, as indicated at step 518. Alternatively, at least onehandwriting profile representation may be assessed for at least one ofthe information fields using the handwriting from at least two of theinformation fields. In other embodiments, at least two handwritingprofile representations may be assessed for at least one of theinformation fields using the handwriting from at least one of theinformation fields. In an embodiment, the handwriting profilerepresentations may be stored on a memory medium on a computer system.

[0129] In some embodiments, the method may further include providing oneor more additional documents to the computer system, a shown by step519. At least one of the additional documents may include at least oneinformation field. At step 521, the method may include updating at leastone of the handwriting profile representations using at least one of theinformation fields of at least one of the additional documents.Alternatively, updating may also use at least one of the informationfields of at least one of the documents. In certain embodiments,updating at least one of the handwriting profile representations mayinclude modifying at least one handwriting profile representation,deleting at least one handwriting profile representation, and/ordetermining at least one handwriting profile representation.

[0130] The additional documents may be payment instruments presented forpayment to a bank that have been validated. The computer system memorymay then be updated with the handwriting profile representationsobtained from the additional documents. Consequently, the profile may beperiodically updated to take into account the dynamic variation of thehandwriting of the one or more account owners.

[0131] A writing profile, such as a payment instrument account profile,as described herein, may be applied to assess fraud in documents, suchas payment instruments presented for payment to a bank. Methods forassessing fraud in a document, such as a payment instrument, may requiremethods for recognizing characters or text on a document. “Recognizing,”as used herein, refers to the process of identifying elements of writteninformation, such as numerals, letters, and symbols, from arepresentation of the characters. The representation may be an image ormathematical representation, for example. Elements of writteninformation may be identified from mathematical representations fromfeature vectors.

[0132] Written information in an image representation may be identifiedby converting the image into a computer processable format, such asASCII. “ASCII” is an acronym for the “American Standard Code forInformation Interchange.” ASCII is a code for representing Englishcharacters as numbers, with each letter assigned a number from 0 to 127.Most computer systems use ASCII codes to represent text to enabletransfer of data from one computer to another.

[0133] Several products are commercially available for recognition ofwritten information in images. For example, Checkscript and Quickstrokesare character recognition software products from Mitek Systems of SanDiego, Calif. In addition, Checkplus 2.0 is character recognitionsoftware provided by Parascript of Niwot, Colo.. A2iA of New York, N.Y.provides CheckReader™. The Corroborative Image Character Recognition(CICR) System may be obtained from Computer Sciences Corporation of ElSegundo Calif.. Gaussian Probabilistic Distribution (GPD) software maybe obtained from Malayappan Shridhar of the School of Engineering at theUniversity of Michigan at Dearborn, Dearborn, Mich..

[0134] In one embodiment, the information field content shape profile,referred to in Table 1, may include at least one mathematicalrepresentation of writing on a computer system. At least one of themathematical representations may represent writing of authorizedwriters, such as one or more account owners. In an embodiment,mathematical representations may include one or more entry types of aninformation field of a document. The mathematical representationscharacterize writing of the authorized writers. The mathematicalrepresentations may be represented in terms of feature vectors, asdescribed herein. In an embodiment, at least one of the mathematicalrepresentations may include at least one variant of an entry type of aninformation field.

[0135] According to one embodiment, an information field content shapeprofile may be generated for any information field of a document thatincludes writing. For example, information field content shape profilesof a checking account (see FIG. 4) may be generated for payee names of apayee field, dates in a date field, amounts in a courtesy amount field,amounts in a legal amount field, descriptions in a memo field, and asignature in a signature field. Additionally, information field contentshape profiles of a giro account (See FIG. 5) may be generated foraccount numbers in a debit account field, amounts in an amount field,descriptions in a description field, account numbers in a credit accountfield, names in a name field, city names in a city field, and signaturesin a signature field.

[0136] Fraud may be assessed in a document by comparing writteninformation in an information field of the document to a writingprofile, such as a payment instrument account profile. According to oneembodiment, a method of comparing written information to a writingprofile using a computer system may include providing the writteninformation from a document to the computer system. The writteninformation may be in the form of a mathematical representation thatincludes one or more sample features. Furthermore, at least one writingprofile representation may be stored in memory on a memory medium. Atleast one writing profile representation may include at least onemathematical representation. At least one mathematical representationmay include one or more profile features. In an embodiment, the samplefeatures and the profile features may include both global features andlocal features.

[0137] The method may further include assessing non-matching featuresfrom a comparison of the sample features and profile features. In someembodiments, the non-matching features may be associated with fraudweights.

[0138] “Match” refers to a degree of similarity between samples ofwritten information. For example, U.S. Pat. Nos. 5,995,953 to Rindtorffet al., U.S. Pat. No. 5,828,772 to Kashi et al., and U.S. Pat. No.5,710,916 to Barbara et al., which are incorporated by reference as iffully set forth herein, disclose methods that include assessing a degreeof similarity between samples of handwritten information based on acomparison of the feature vectors of the samples of handwritteninformation.

[0139] According to one embodiment, determining whether a sample ofwritten information matches a profile includes both “global matching”and “local matching” of features. Generally, global matching refers toassessing whether written information may belong to a set ofindividuals, such as one or more account owners, based on globalcharacteristics. In global matching, global features, such as slant,tangent, and curvature entropy, in feature vectors of samples may becompared to assess whether features match. Global matching may beapplied, for example, in assessing whether a payee name entry in a payeefield and a legal amount entry in a legal amount field were written bythe same person.

[0140] Furthermore, local matching refers to assessing whether twosamples of written information correspond to the same character, word,or set of words and characters. Local matching may be applied, forexample, in assessing whether a signature was written by an accountowner. The signature may be compared to writing profile representationsof signatures of one or more account owners. Local matching may employmathematical techniques such as K-nearest neighbor and neural networks.For instance, in the case of a neural network applied to the legalamount field, a sample of written information and all of the variants of“One Hundred” may be converted into feature vectors. A neural net maythen be trained using a standard back propagation training algorithm toassess whether the sample of written information matches at least one ofthe variants.

[0141] In certain embodiments, the writing profile may be used to assessa document, such as a payment instrument that is presented to a bank forpayment. A method depicted in FIG. 16 of assessing a document mayinclude providing a document to the computer system, as shown at step418. The document may include at least one information field. In anotherembodiment, the document may include at least two information fields.The method may further include comparing writing in at least two of theinformation fields of the document to at least one writing profilerepresentation, as shown at step 420. At least one writing profilerepresentation may be from at least one information field of at leastone other document. In an embodiment, at least the one other documentmay be a valid document. In another embodiment, the method may includecomparing writing in at least one of the information fields of thedocument to at least one writing profile representation from at leasttwo information fields of at least one other document. Alternatively,writing in at least one of the information fields of the document may becompared to at least two writing profile representations from at leastone information field of at least one other document.

[0142] As depicted in step 422, fraud in the document may be assessedusing at least one of the comparisons. In some embodiments, evidence offraud may include a failure of at least a portion of the writing in atleast one of the information fields of the document to approximatelymatch at least one writing profile representation. Alternatively,evidence of fraud may be a failure of at least a portion of the writingin at least two of the information fields of the document toapproximately match at least one writing profile representation.

[0143] In certain embodiments, the information field content shapeprofile may be used to assess fraud in a document, such as a paymentinstrument that is presented to a bank for payment. A method depicted inFIG. 17 may include obtaining at least one mathematical representationof the writing from information fields of the document, as shown at step540. In an embodiment, mathematical representations may be obtained byconverting images of the written information. At least one of themathematical representations may correspond to an example of a type ofwritten information and/or an entry type of an information field. Themethod may further include providing access to a computer system thatincludes a writing profile, as shown by step 542. In an embodiment, thewriting profile may include at least one writing profile representationof writing from one or more valid documents. At least one writingprofile representation may correspond to at least one variant of a typeof written information and/or an entry type of an information field. Atstep 544, at least one of the mathematical representations of thehandwriting may be compared to one or more of the handwriting profilerepresentations to assess whether the written information approximatelymatches the profile. If the written information does not approximatelymatch the information field content shape profile, the computer maygenerate a flag indicating that the document is potentially fraudulent.

[0144] In one embodiment, the information field content image profile,referred to in Table 1, may include at least one image of writing on acomputer system. At least one of the images may correspond to writing ofauthorized writers, such as one or more account owners. In anembodiment, at least one of the images may correspond to one or moreentry types of an information field of a document. At least one of theimages may characterize the writing of authorized writers, such as theone or more account owners. In an embodiment, at least one of the imagesmay correspond to at least one variant of an example of a type ofwritten information or an entry type of an information field. Accordingto one embodiment, as described in reference to the information fieldcontent shape profile, an information field content image profile may begenerated for at least one information field of a document.

[0145] In some embodiments, the information field content image profilemay be used to assess fraud in a document, such as a payment instrumentthat is presented to a bank for payment. A method depicted in FIG. 18may include obtaining at least one image of writing from informationfields of the document, as shown at step 546. At least one image maycorrespond to examples of types of written information and/or entrytypes of one or more of the information fields. The method may furtherinclude providing access to a computer system that includes a writingprofile, as shown by step 548. In an embodiment, the writing profile mayinclude at least one handwriting profile representation from one or moredocuments. At least one writing profile representation may correspond toat least one variant of a type of written information and/or an entrytype of an information field. At step 550, at least one of the images ofthe writing may be compared to at least one writing profilerepresentation to assess whether the writing approximately matches thewriting profile. If the writing does not approximately match theinformation field content image profile, the computer may generate aflag indicating that the document is potentially fraudulent.

[0146] Assessing a degree of similarity or matching of images of writteninformation may be performed by several methods. These methods determinethe degree of similarity between two images of a type of writteninformation such as a type of character or set of characters, forexample, a signature. U.S. Pat. No. 6,249,604 to Huttenlocher et al.,which is incorporated by reference as if fully set forth herein,discusses one such method based on the technique of dynamic warping.U.S. Pat. No. 6,157,731 to Hu et al., which is incorporated by referenceas if fully set forth herein, describes another such method that useshidden Markov models.

[0147] Computer software that determines a degree of similarity ofimages of a type of written information may be obtained commercially.Glory Signature Verification Software (GSVS) from Glory Ltd. HIMEJI,HYOGO, Japan determines, with a degree of certainty, whether the sameindividual wrote two images of a type of handwritten information.

[0148]FIGS. 18 and 19 illustrate assessment of fraud in the signaturefield of a giro. In FIG. 19, illustrations 438 include a set of samplesof handwritten information that correspond to handwriting profilerepresentations for the signature field of a giro account. The samplesof handwritten information may be stored as mathematical representationsin an information field content shape profile. Alternatively, thesamples of handwritten information may be stored as images in aninformation field content image profile. Samples 440, 442, 444, and 446are signatures from different giros. A comparison of samples 440-446 tothe profile representations 438 may indicate that it is likely that eachof the samples is fraudulent.

[0149] Furthermore, in FIG. 20 illustrations 448 include a set ofsamples of handwritten information that correspond to profilerepresentations for the signature field of another giro account. Sample450 is a signature from a giro. Sample 450 may likely be assessed to bea fraudulent signature based on a comparison with illustrations 448.

[0150]FIG. 21 is an illustration of fraud assessment in the courtesyamount field of a check of a checking account. Samples 452 represententries in a courtesy amount field from valid checks of the checkingaccount. Sample 454 represents a courtesy amount field of a check to bevalidated. Information field content shape analysis may likely flagsample 454 as potentially fraudulent due to the raised ‘00’ and thestyle of the ‘5.’

[0151]FIG. 22 is an illustration of fraud assessment in a giro. In FIG.22, the pen thickness of the signature in signature field 436 issignificantly thinner than the entries in the other fields, for example,amount field 434. Therefore, the giro may likely be flagged aspotentially fraudulent.

[0152]FIG. 23 is an illustration of fraud assessment in the city fieldof a giro. Samples 456 are entries for a city field from valid giros ofan account. Information field content shape analysis may recognizesample 458 as the same city as the entries in samples 456. However,information field content shape analysis may likely demonstrate that thehandwriting is different than the valid entries. As a result, the giromay likely be flagged as potentially fraudulent.

[0153] In one embodiment, the digit/alpha profile, referred to in Table1, may include one or more sets of written characters on a computersystem. The one or more sets may correspond to one or more charactertypes. In addition, the one or more character types may correspond toone or more types of numerals. The one or more character types may alsocorrespond to one or more types of letters of an alphabet. A set ofwritten characters may include at least one variant of a writtencharacter type. The variants of a character type may characterize thewriting features of the character type of authorized writers, such asone or more account owners. For example, a set of handwritten ‘3’s mayrepresent variations in the way an account owner writes a ‘3.’ In anembodiment, the written characters in the one or more sets may be storedas mathematical representations, as described herein, on a memorymedium. Alternatively, the written characters in the one or more setsmay be stored as images.

[0154] In certain embodiments, the digit/alpha profile may be used toassess fraud in a document, such as a payment instrument that ispresented to a bank for payment. A method depicted in FIG. 24 mayinclude obtaining one or more samples of the writing, as shown at step552. One or more of the samples may include one or more images. In anembodiment, the handwriting in the information fields may include one ormore written characters. The method may further include, as shown bystep 554, recognizing one or more written characters in one or moreimages of the writing in the information fields. The written charactersmay correspond to at least one character type. The method may furtherinclude providing access to a computer system that includes a writingprofile, as shown by step 556. In an embodiment, the writing profile mayinclude one or more writing profile representations from at least oneother documents. The at least one other document may be a validdocument. In an embodiment, the one or more writing profilerepresentations may include at least one variant of a type of writtencharacter. At step 558, one or more of the written characters may becompared to at least one profile representation of written characters toassess whether the written characters approximately match the writingprofile. If one or more of the written characters do not approximatelymatch the digit/alpha profile, the computer may generate a flagindicating that the document is potentially fraudulent.

[0155] In an alternative embodiment, the method may include convertingone or more of the images of the written information to one or moremathematical representations, as described herein. The one or moremathematical representations may include one or more mathematicalrepresentations of written characters. One or more written charactersmay be recognized from the one or more mathematical representations. Themethod may further include comparing the mathematical representations ofwritten characters to at least one writing profile representation ofwritten characters to assess whether the written charactersapproximately match the writing profile.

[0156]FIG. 25 is an illustration of converting a character in ahandwriting image to a mathematical representation. Set of images 352represent several variations of a handwritten ‘3.’ At step 354, thecharacter may be recognized using character recognition software. Thestrokes of the character may then be analyzed at step 356. In this case,the numeral ‘3’ includes two strokes: upper cusp 360 and lower cusp 362.The shape of the character may be classified at step 358 using neuralnet or k-nearest neighbor techniques.

[0157]FIG. 26 illustrates assessment of fraud in a numeric informationfield of a payment instrument using the methods described herein. List400 includes a digit/alpha profile for a payment instrument account fornumeric characters from zero to nine. The profile includes variants ofthe account for each numeric type. Sample 402 corresponds to a numericentry from a field of a payment instrument. Numerals 404 were recognizedas the numeral ‘8’ in the sample. The ‘8’s do not appear to match thevariants 406 of the numeral ‘8’ in the profile.

[0158] In one embodiment, the symbology profile, referred to in Table 1,may include one or more written symbols on a computer system. The one ormore written symbols may correspond to one or more symbol types. Forexample, the one or more symbol types may include, for example, one ormore types of punctuation marks and/or a ‘+’. At least one of thewritten symbols may include at least one variant of a type of writtensymbol. The variants of a symbol type may characterize the writingfeatures of the symbol type of designated individuals, such as one ormore account owners of a payment instrument account. For example, one ormore handwritten ‘+’s represent variations in the way an account ownermay write a ‘+’ in the legal amount field. In an embodiment, the writtensymbols in the symbology profile may be stored as mathematicalrepresentations, as described herein, on a memory medium. Alternatively,the written symbols in the symbology profile may be stored as images.FIG. 10 illustrates symbology in handwriting samples in the legal amountfield. The symbol types in the legal amount field include symbol 350, a‘+’, and symbol 349, ‘00/100’.

[0159] In certain embodiments, the symbology profile may be used toassess fraud in a document, such as a payment instrument that ispresented to a bank for payment. A method depicted in FIG. 27 mayinclude obtaining one or more samples of the writing, as shown at step560. One or more of the samples may include one or more images. In anembodiment, the writing in the information fields may include one ormore written symbols. The method may further include, as shown by step562, recognizing one or more written symbols in one or more images ofthe writing in the information fields. The written characters maycorrespond to one or more symbol types. The method may further includeproviding access to a computer system that includes a writing profile,as shown by step 564. In an embodiment, the writing profile may includeone or more writing profile representations from one or more documents.In an embodiment, one or more of the handwriting profile representationsmay include at least one variant of types of written symbols. At step566, one or more of the written symbols may be compared to one or moreof the writing profile representations of written characters to assesswhether the written symbols approximately match the profile. If one ormore of the written symbols do not approximately match the symbologyprofile, the computer may generate a flag indicating that the documentis potentially fraudulent.

[0160] In an alternative embodiment, the method may include convertingone or more of the images of the writing to one or more mathematicalrepresentations, as described herein. One or more of the mathematicalrepresentations may include one or more mathematical representations ofwritten symbols. One or more written symbols may be recognized from oneor more of the mathematical representations. The method may furtherinclude comparing the mathematical representations of written symbols toone or more of the writing profile representations of written symbols toassess whether the written symbols approximately match the profile.

[0161]FIG. 28 is an illustration of assessment of fraud for a check of achecking account. Sample 392 represents an entry in a legal amount fieldof a check to be validated. Samples 394 represent entries in the legalamount field from valid checks from the checking account. Sample 392 maybe determined to be potentially fraudulent by the methods describedherein. First, information field content shape analysis may demonstratethat the writing of the dollar amount of sample 392 does not matchsamples 394. In addition, there are several differences in symbology.For example, line 391 is different than line 393. The ‘xx/100’ symboldiffers as shown by comparing symbol 397 and symbol 399. Samples 394include line 395, which is absent from sample 392.

[0162] In one embodiment, the syntax pattern profile, referred to inTable 1, may include at least one syntax pattern. A syntax pattern mayinclude one or more elements. The one or more elements in a syntaxpattern may be in a specific order. For example, the entries in the datefield of a check may include a month of the year, a date of the month, ayear, and punctuation marks. At least one syntax pattern in the writingprofile may include at least one variant of a syntax pattern for aninformation field. For instance, at least one variant may be the mannerthat one or more account owners enter a date in the date field. Forexample, a date may be written several ways: 2/14/01, 2-14-01, Feb. 14,2001, and 14 February 01. According to one embodiment, elements of thedate field may include: a numeric month, an alphabetic month, a numericdate of the month, a two-digit year, a four-digit year, a comma, aforward slash, and a dash.

[0163] In one embodiment, the syntax pattern profile may be used toassess fraud in a document, such as a payment instrument that ispresented to a bank for payment. A method depicted in FIG. 29 mayinclude obtaining written information in an information field, as shownat step 568. The written information may include one or more elements.In some embodiments, the written information may be an image and themethod may include recognizing one or more of the elements in the image.One or more of the elements may include, for example, written charactersand symbols that may appear in the date field of a payment instrument.Alternatively, the written information may be a mathematicalrepresentation, as described herein, and the method may includerecognizing one or more of the elements from the mathematicalrepresentation. An order of one or more of the elements in theinformation field of the document may then be assessed, as shown by step570. The method may further include, as shown by step 572, providingaccess to a computer system that includes a writing profile. Thehandwriting profile may include one or more writing profilerepresentations of writing from one or more valid documents. In anembodiment, the information field may be a date field and the writingprofile representations may include variants of a syntax pattern, suchas a written date. The method may further include comparing the elementsand the order of the one or more elements to one or more writing profilerepresentations to assess whether the elements and the order of the oneor more elements approximately match the writing profile, as shown bystep 574. If the elements and the order of the elements do notapproximately match the syntax pattern profile, the computer maygenerate a flag indicating that the payment instrument is potentiallyfraudulent.

[0164]FIG. 30 illustrates fraud assessment in a date field of a paymentinstrument. Samples 388 include several examples of a date field frompayment instruments for an account. The date field for the accountalmost consistently appears as an alphabetic month, followed by anumeric day, and a two-digit year. Date 389 is the only sample that isinconsistent. Sample 390 is a date field from a payment instrument to betested for fraud. The syntax pattern of sample 390 approximately matchesonly date 389 of samples 388. In addition, the slant of sample 390 doesnot match date 389. Therefore, sample 390 may be potentially fraudulent.

[0165] In one embodiment, the lexicon profile, referred to in Table 1,may include one or more lexicon names for an information field of adocument, such as a payment instrument. A lexicon name refers to aspecific word or set of characters or symbols that has been previouslyrecognized in documents associated with authorized writers, such as oneor more account owners of a payment instrument account. In oneembodiment, the list of lexicon names may be stored in memory in acomputer processable format such as a ASCII format. For example, lexiconnames for a payee field may include payee names that have previouslyappeared on checks of a checking account. Another example may includelexicon names for the city field of a giro account. In one embodiment,the lexicon profile may include a frequency associated with a lexiconname. The frequency may be a measure of the how often a lexicon nameappears on a payment instrument of the account. The frequency may beexpressed as a percentage of payment instruments associated with one ormore of the lexicon names over a particular time period. For example, alexicon name for a payee field may have appeared on 21% of checkswritten over a six month period. In some embodiments, a frequency may beassociated with a subset of the lexicon names for an information field.For instance, a frequency may be associated with the top ten payee namesthat appear on payment instruments of the account.

[0166] In certain embodiments, the lexicon profile may be used to assessfraud in a document, such as a payment instrument that is presented to abank for payment. A method for assessing fraud is depicted in FIG. 31.In an embodiment, a method of assessing fraud in a document using acomputer system may include obtaining writing in an information field ofthe document, as shown in step 576. The method may further includerecognizing an entry from the written information in the informationfield of the document, as indicated in step 578. The entry may includeone or more characters or symbols. The information field may be, forexample, a payee field of a check and the entry may be a payee name.Alternatively, the field may be the city field of a giro and the entrymay be a city name. The method may further include, as shown by step580, providing access to a computer system that includes a writingprofile. The writing profile may include one or more lexicon names forone or more information fields from one or more valid documents. In anembodiment, the method may also include comparing the entry to one ormore of the lexicon names for the information field in the writingprofile to assess whether the entry matches the writing profile, asshown in step 582. If the entry does not approximately match the writingprofile, the computer may generate a flag indicating that the documentis potentially fraudulent.

[0167] In some embodiments, the method may include determining afrequency associated with the entry if the entry approximately matchesat least one of at least one of the lexicon names. If the frequency isbelow a certain level, the computer may generate a flag indicating thatthe payment instrument is potentially fraudulent. In another embodiment,the method may include assessing whether the entry is a member of asubset of lexicon names that are associated with a particular frequency.If the entry is not a member of the subset, the computer may generate aflag indicating that the document is potentially fraudulent.

[0168]FIG. 32 illustrates fraud assessment in a city field of a giro.Samples 428 represent variants of the city name “Bunschoten” for a cityfield of a giro account. An information field content shape orinformation field content image profile may include samples 428. Sample430 is an entry in a city field of a giro. Sample 430 does not appear toapproximately match samples 428 and was recognized as “Bilthoven.”Lexicon 432 represents a lexicon profile that includes a list of citynames that have previously appeared on giros of the account. Each cityname includes a number in parenthesis indicating the number of giros onwhich the city name has appeared. Sample 430 also appears to fail toapproximately match the lexicon profile. Therefore, the giro may bepotentially fraudulent.

[0169]FIG. 33 is an illustration of fraud assessment in the memo fieldof a giro of a giro account. List 464 represents a list of entries thatappeared in the memo field of valid giros of a giro account. List 464may correspond to a lexicon profile. Sample 466 is an entry in the memofield of a giro to be validated. A comparison of sample 466 with thelist indicates the sample is not consistent with the account. Therefore,the giro is potentially fraudulent.

[0170] In some embodiments, the information field cross correlationprofile, referred to in Table 1, may include cross-field relationshipsfor a document, such as a payment instrument account on a computersystem. In particular, a writing profile may include one or more firstlexicon names associated with a first information field of a paymentinstrument of the account on a computer system. At least one of one ormore of the first lexicon names may be associated with one or moresecond lexicon names associated with a second information field. Thefirst lexicon name may include an entry type of the information fieldand the second lexicon name may include an entry type of the secondinformation field. The cross-field relationships in the writing profilemay include relationships between information fields that occur with aparticular frequency in a document, for example, in payment instrumentsof an account. In this manner, an entry in one information field may beused to predict a likely entry type in another information field. In oneembodiment, a frequency of a particular cross-field correlation in anaccount may be included in the profile.

[0171] Several types of relationships between information fields mayoccur frequently in payment instrument accounts. For example, aparticular account number entered in a memo field may be correlated witha payee name in the payee field of a check. Also, a payee name may becorrelated with a particular courtesy amount. In addition, the identityof one account owner of a joint account, obtained from the signaturefield, may be correlated with a syntax pattern in the date field.

[0172] In certain embodiments, the information field cross correlationprofile may be used to assess fraud in a document, such as a paymentinstrument that is submitted to a bank for payment. A method forvalidating a payment instrument is depicted in FIG. 34. The method mayinclude assessing whether a first entry in a first information fieldapproximately matches one or more first lexicon names in a handwritingprofile for the first information field, as shown by step 584. Themethod may further include, as shown by step 586, obtaining writing in asecond information field of the document. Access may then be provided tothe computer system that includes a writing profile, as shown by step588. The handwriting profile may include cross-field correlations fromone or more valid documents. The method may further include comparingthe second entry to a second lexicon name of one or more second lexiconnames associated with the approximately matching first lexicon name inthe first information field. The comparison may be used to assesswhether the second entry approximately matches a second lexicon name, asshown by step 590. If the second entry does not match a second lexicon,the computer may generate a flag indicating that the document may bepotentially fraudulent. In another embodiment, the frequency that thefirst lexicon name approximately matches the second lexicon name may beconsidered in fraud assessment of the document.

[0173] An embodiment of a method of assessing information in at leastone information field in a document is depicted by a flow chart in FIG.35. In an embodiment, one or more of the documents may be a paymentinstrument. The information fields may include writing of one or moreaccount owners of a payment instrument account. The method may includeproviding a document to the computer system, as shown in step 598 ofFIG. 35. In one embodiment, the documents may include at least oneinformation field. In other embodiments, the document may include atleast two information fields. The document may be provided by accessingat least one image from a database in memory on a computer system. Incertain embodiments, the method may include obtaining information onwriting in an information field of the document, as shown in step 600.In some embodiments, obtaining information on writing may includerecognizing written information in an information field. For example, apayee name in a payee field of a payment instrument may be recognized.

[0174] As shown in step 602, the method may further include comparingthe obtained written information in the information field and writteninformation in at least one other information field to at least onewriting profile representation from at least one other document.Alternatively, the obtained written information in the information fieldand written information in at least two other information fields may becompared to at least one handwriting profile representation from atleast one other document. In other embodiments, the method may includecomparing the obtained written information in the information field andwritten information in at least one other information field to at leasttwo writing profile representations from at least one other document.

[0175] In an embodiment, at least one of the other documents may bevalid. In certain embodiments, at least one of the writing profilerepresentations may include written information from the informationfield and written information from at least one of the other informationfields. The written information from the information fields may be fromat least one of the other documents. For example, at least one writingprofile representation may include a cross-field correlation between theinformation field and at least the one other information field. In oneembodiment, the cross-field correlation may include a lexicon name forthe information field and at least one lexicon name for at least the oneother information field.

[0176] In some embodiments, at least one of the comparisons of writteninformation may be used to verify the obtained written information, asshown in step 604. In one embodiment, written information may beverified by assessing whether the obtained written information in theinformation field and the written information in at least one otherinformation field approximately matches at least one writing profilerepresentation from at least one other document.

[0177] In some embodiments, the document stock or pre-printed profile,referred to in Table 1, may include one or more stock characteristics orpre-printed information of a document associated with authorizedwriters, such as one or more account owners. For example, the stockcharacteristics may characterize a layout of the payment instrumentcorresponding to the account. Stock characteristics may include one ormore graphics elements, as well as their location, on the paymentinstrument of the account. Graphics elements may include, for example,bank logos. The size of the payment instrument of the account may alsobe a stock characteristic. In addition, stock characteristics mayinclude one or more machine-printed text blocks, along with theirlocations. Text blocks may include, for example, an address of one ormore account owners and account numbers. The document stock profile mayinclude a machine-printed profile analogous to a variable writingprofile for information fields. The machine-printed profile may includemathematical representations and/or images of machine-printed text inthe text blocks of documents.

[0178]FIG. 36 depicts stock characteristics of a check 340. Label 338indicates graphics elements that consist of logos. Label 342 indicatesan account/routing order number. In addition, label 344 is the name andaddress of the account owner.

[0179] In certain embodiments, a pre-printed profile may be used toassess a document, such as a payment instrument that is presented to abank for payment. A method depicted in FIG. 37 of assessing a documentmay include providing a document to the computer system, as shown atstep 530. The document may include at least one information field. Inanother embodiment, the document may include at least two informationfields. The method may further include comparing pre-printed informationin at least two of the information fields of the document to at leastone pre-printed profile representation from at least information fieldof at least one other document, as shown in at step 532. In anembodiment, at least the one other document may be a valid document. Inanother embodiment, the method may include comparing pre-printedinformation in at least one of the information fields of the document toat least one pre-printed profile representation from at least twoinformation fields of at least one other document. Alternatively,pre-printed information in at least one of the text blocks of thedocument may be compared to at least two pre-printed profilerepresentations from at least one text block of at least one otherdocument.

[0180] Pre-printed information may include, but is not limited to amathematical representation and/or an image. Pre-printed information mayalso include, but is not limited to at least one type of pre-printedinformation such as a word type, character type and/or graphic element.Pre-printed information may further include, but is not limited to aglobal feature of pre-printed information and a local feature ofpre-printed information.

[0181] As depicted in step 534, fraud in the document may be assessedusing at least one of the comparisons. In some embodiments, potentialfraud may be indicated by a failure of at least a portion of thepre-printed information in at least one of the information fields of thedocument to approximately match at least one pre-printed profilerepresentation. Alternatively, potential fraud may be indicated by afailure of at least a portion of the pre-printed information in at leasttwo of the information fields of the document to approximately match atleast one pre-printed profile representation.

[0182]FIG. 38 illustrates fraud assessment in a giro. Text 424represents machine-printed text that is a stock characteristic from agiro of an account. Text 425 and 426 represent the correspondingmachine-printed text from giros presented for payment. The size of text424 is 260 pixels×80 lines. Text 425 and 426 have a size of 350pixels×95 lines. The inconsistency may indicate potential fraud.

[0183] In certain embodiments, fraud may be assessed from variances inwriting within an information field of a document, such as a paymentinstrument. Variances may occur when a forger alters a specific portionof a document, such as a payment instrument. For example, a forger mayalter the amount in the courtesy amount field by writing in one or moreadditional numbers. Therefore, an information field may contain writtencharacters of the same type written both by a forger and by designatedindividuals, such as one or more of the account owners.

[0184]FIG. 39 depicts an embodiment of a method of assessing a document,such as a payment instrument using a computer system. The method mayinclude providing a document to a computer system, as shown in step 592.In an embodiment, the document may include at least one informationfield. In some embodiments, writing in at least one of the informationfields of the document may include at least two examples of a type ofwritten information. Writing may include, but is not limited to types ofcharacters, words, symbols, and/or other writing features. Other writingfeatures may include, but are not limited to local slant, global slant,or pen thickness. For example, written characters may include one ormore character types, as described herein. An information field mayinclude written characters of the same character type. For instance, acourtesy amount field of a payment instrument may read ‘3,740.53.’ Thiscourtesy amount field includes two examples of a type of writteninformation, the first ‘3’ and the second ‘3.’ In some embodiments, thewriting may include an image. Alternatively, the writing may include amathematical representation. In an embodiment, examples of types ofwritten information may be recognized from an image and/or amathematical representation.

[0185] The method may further include, as shown by step 594, comparingat least two of the examples of the type of written information. At step596, the method may additionally include assessing whether two or moreof the examples approximately match. In the case cited above, the firstand the second ‘3’ may be compared to assess whether they match. Oneembodiment may include comparing images of examples of a type of writteninformation with image comparison software. Alternatively, examples of atype of written information may be converted to mathematicalrepresentations. In this case, the handwriting features of themathematical representations may be compared. If at least two of theexamples of written information do not approximately match, the computermay generate a flag indicating that the document may be potentiallyfraudulent.

[0186] In some embodiments, the method may further include comparing theexamples of types of written information to at least one writing profilerepresentation. For example, examples of types of handwritten charactersmay be compared to writing profile representations in the digit/alphaprofile.

[0187] Some embodiments may include a method of assessing fraud fromvariances in writing between different information fields of a document.In one embodiment, a document may include at least two informationfields. At least two information fields of the document include at leastone example of a type of written information. The method may includecomparing at least two of the examples in at least two of theinformation fields. The method may further include assessing whether twoor more of the examples approximately match. For instance, numerals in adate field and numerals in a courtesy amount field of a paymentinstrument may be compared. For example, a courtesy amount field mayread ‘3,340.53.’ A date field of the same payment instrument may read‘Jan. 4, 2003.’ Both the courtesy field and the date field includeexamples of a written ‘4,’ which may be compared. If at least two of theexamples of the type of written information do not approximately match,the computer may generate a flag indicating that the document may bepotentially fraudulent.

[0188]FIG. 40 is an illustration of assessing fraud from variations inhandwriting in the same information field and between differentinformation fields of a giro. FIG. 40a depicts amount field 372 andcredit account field 374 from a giro. Two apparent differences may beassessed between the two information fields. First, there is adifference in ink thickness between information fields. Second, there isa difference in the style of the ‘6’ between the two information fields.

[0189]FIG. 40b depicts amount field 376 and credit account field 378from a giro. There is a difference in style between the two ‘5’s in theaccount field. In addition, there is a difference in style of the ‘2’ isthe amount field and the ‘2’ in the account field.

[0190]FIG. 40c depicts amount field 380 and credit account field 382from a giro. Variations exist in ink thickness for digits in the accountfield. In addition, there is a variation in slant for digits in theaccount field. Also, a difference in style is exhibited for the ‘3’ and‘5’ between the amount and account fields.

[0191]FIG. 40d depicts amount field 384 and credit account field 386from a giro. There are differences in style for the ‘7’, ‘2’, and ‘4’between the amount and account field. In addition, there is a variationin slant between digits in the account field.

[0192] In one embodiment, a handwriting profile, such as a paymentinstrument account profile, may include a database of previouslyidentified forgers. The database may further include a forger writingprofile for one or more identified forgers. The forger writing profileis analogous to the writing profile for authorized writers, such as oneor more account owners, shown in Table 1. A forger profile may includeat least some writing profile information obtained from previouslyidentified forged documents, such as payment instruments, associatedwith a forger.

[0193]FIG. 41 depicts an embodiment of a method for identifying adocument comprising forged information using a computer system. Themethod may include providing a document to the computer system, as shownby step 612. In one embodiment, the document may include at least oneinformation field. In another embodiment, the document may include atleast two information fields. As shown at step 614, the method mayfurther include comparing writing in at least two of the informationfields of the document to at least one forger writing profilerepresentation from at least one information field. In anotherembodiment, the method may include comparing writing in at least one ofthe information fields of the document to at least one forger writingprofile representation from at least two information fields of at leastone forged document. In some embodiments, writing in at least one of theinformation fields of the document may be compared to at least oneforger writing profile representation from at least two informationfields of at least one forged document

[0194] Additionally, as shown at step 615, the method may includeidentifying the document as a document comprising forged informationfrom an approximate match of at least one forger writing profilerepresentation with writing in the document. The method may furtherinclude identifying the forger of the document from the forger writingprofile if the document is identified as forged, as indicated by step616.

[0195] For many financial services companies keying labor represents alarge data capture cost. For example, one of the most expensive keyingoperations is the keying of data from a written information field, suchas a payee name. Keying labor may be reduced through application of awriting profile, such as a payment instrument account profile. Aprofile, as described in Table 1, may be used to capture writteninformation in information fields of documents, such as paymentinstruments presented for payment.

[0196]FIG. 42 depicts an embodiment of a method of capturing writteninformation from an information field of a document using a computersystem. The method may include providing a document to the computersystem, as shown by step 618. In one embodiment, the document mayinclude at least one information field. In one embodiment, the documentmay include at least two information fields. The method may furtherinclude assessing whether writing in an information field approximatelymatches a writing profile representation from at least one informationfield from at least one other document, as indicated by step 620. In anembodiment, the matching writing profile representation is associatedwith a corresponding text representation. In some embodiments, the textrepresentations may be stored in memory in a computer processable formaton the computer system. For example, the computer processable format maybe ASCII format. As shown by step 622, the information field may then beassociated with the text representation corresponding to the matchingwriting profile representation. The writing in the information field maybe a mathematical representation, as described herein. The matching textrepresentation may be assessed from features included in writing profilerepresentations that include mathematical representations from thewriting profile.

[0197]FIG. 43 illustrates the capture of an entry in an informationfield of a payment instrument. Samples 368 represent entries in payeefields extracted from valid checks. The entries may be included in achecking account profile and may be stored as mathematicalrepresentations. Sample 370 is an entry in a payee field of a check isto be captured. Sample 370 may be identified as “NORTHGATE HIGH SCHOOL”from the handwriting features in the mathematical representations insamples 368.

[0198] Further modifications and alternative embodiments of variousaspects of the invention may be apparent to those skilled in the art inview of this description. Accordingly, this description is to beconstrued as illustrative only and is for the purpose of teaching thoseskilled in the art the general manner of carrying out the invention. Itis to be understood that the forms of the invention shown and describedherein are to be taken as the presently preferred embodiments. Elementsand materials may be substituted for those illustrated and describedherein, parts and processes may be reversed, and certain features of theinvention may be utilized independently, all as would be apparent to oneskilled in the art after having the benefit of this description of theinvention. Changes may be made in the elements described herein withoutdeparting from the spirit and scope of the invention as described in thefollowing claims.

1-543. (cancelled).
 544. A method of identifying a document comprisingforged information using a computer system, comprising: providing thedocument to the computer system, wherein the document comprises at leasttwo information fields; comparing writing in at least two of theinformation fields of the document to at least one forger writingprofile representation from at least one information field of at leastone document comprising forged information; and identifying the documentas a document comprising forged information from an approximate match ofat least one forger writing profile representation with handwriting inthe document.
 545. The method of claim 544, further comprising comparingwriting in at least two of the information fields of the document to atleast one forger writing profile representation from at least twoinformation fields of at least one document comprising forgedinformation.
 546. The method of claim 544, further comprising comparingwriting in at least two of the information fields of the document to atleast two forger writing profile representations from at least oneinformation field of at least one document comprising forgedinformation.
 547. The method of claim 544, further comprising comparingwriting in at least two of the information fields of the document to atleast two forger writing profile representations from at least twoinformation fields of at least one document comprising forgedinformation.
 548. The method of claim 544, wherein at least one forgerwriting profile representation is a member of a forger writing profileassociated with a known forger.
 549. The method of claim 544, wherein atleast one forger writing profile representation is a member of a forgerwriting profile associated with a known forger, and further comprisingidentifying the forger of the document from the forger writing profileif the document is identified as forged.
 550. The method of claim 544,wherein at least one forger writing profile representation comprises aforger identity and writing of the forger, and further comprisingidentifying a forger of the document from the forger writing profile.551. The method of claim 544, wherein providing the document to thecomputer system comprises providing images of the document to thecomputer system.
 552. The method of claim 544, further comprisingassessing fraud in the document using at least one of the comparisons.553. The method of claim 544, further comprising assessing fraud in thedocument using at least two of the comparisons.
 554. The method of claim544, wherein comparing writing comprises comparing at least onecharacteristic of the writing.
 555. The method of claim 544, furthercomprising assessing fraud in the document using at least one of thecomparisons, wherein evidence of fraud comprises at least a portion ofthe writing in at least two of the information fields of the document toapproximately match at least one forger writing profile representation.556. The method of claim 544, further comprising assessing fraud in thedocument using at least two of the comparisons, wherein evidence offraud comprises at least a portion of the writing in at least one of theinformation fields of the document to approximately match at least oneforger writing profile representation.
 557. The method of claim 544,further comprising comparing writing in at least two of the informationfields of the document to at least one forger writing profilerepresentation from at least two information fields of at least oneforged document.
 558. The method of claim 544, further comprisingcomparing writing in at least two of the information fields of thedocument to at least two forger writing profile representations from atleast one information field of at least one forged document.
 559. Themethod of claim 544, further comprising comparing handwriting in atleast two of the information fields of the document to at least twoforger writing profile representations from at least two informationfields of at least one forged document.
 560. The method of claim 544,wherein at least one of the documents comprises a payment instrument.561. The method of claim 544, wherein providing the document to thecomputer system comprises obtaining images of writing of at least oneinformation field.
 562. The method of claim 544, further comprisingcreating a mathematical representation of the writing in at least oneinformation field.
 563. The method of claim 544, wherein the writingcomprises at least one image.
 564. The method of claim 544, wherein thewriting comprises at least one type of written information.
 565. Themethod of claim 544, wherein the writing comprises at least one type ofwritten information, and wherein at least one type of writteninformation comprises a word type.
 566. The method of claim 544, whereinthe writing comprises at least one type of written information, andwherein at least one type of written information comprises a charactertype.
 567. The method of claim 544, wherein the writing comprises atleast one global feature of the writing.
 568. The method of claim 544,wherein the writing comprises at least one local feature of the writing.569. The method of claim 544, wherein the writing comprises at least onesyntax pattern.
 570. The method of claim 544, wherein the writingcomprises at least one lexicon name for at least one information field.571. The method of claim 544, wherein at least one forger writingprofile representation comprises at least one mathematicalrepresentation.
 572. The method of claim 544, wherein at least oneforger writing profile representation comprises at least one image. 573.The method of claim 544, wherein at least one forger writing profilerepresentation comprises at least one type of written information. 574.The method of claim 544, wherein at least one forger writing profilerepresentation comprises at least one writing variant of an example ofat least one type of written information.
 575. The method of claim 544,wherein at least one forger writing profile representation comprises atleast one writing variant of an example of at least one type of writteninformation, and wherein at least one type of written informationcomprises a word type.
 576. The method of claim 544, wherein at leastone forger writing profile representation comprises at least one writingvariant of an example of at least one type of written information, andwherein at least one type of written information comprises a charactertype.
 577. The method of claim 544, wherein at least one forger writingprofile representation comprises at least one writing variant of anexample of at least one type of written information, and furthercomprising determining at least one of the variants with a clusteralgorithm.
 578. The method of claim 544, wherein at least one forgerwriting profile representation comprises at least one globalcharacteristic of the writing.
 579. The method of claim 544, wherein atleast one forger writing profile representation comprises at least onelocal characteristic of the writing.
 580. The method of claim 544,wherein at least one forger writing profile representation comprises atleast one variant of a syntax pattern.
 581. The method of claim 544,wherein at least one forger writing profile representation comprises atleast one lexicon name for at least one information field.
 582. Asystem, comprising: a CPU; a data memory coupled to the CPU; and asystem memory coupled to the CPU, wherein the system memory isconfigured to store one or more computer programs executable by the CPU,and wherein the computer programs are executable to implement a methodfor identifying a document comprising forged information, the methodcomprising: providing the document to the computer system, wherein thedocument comprises at least two information fields; comparing writing inat least two of the information fields of the document to at least oneforger writing profile representation from at least one informationfield of at least one document comprising forged information; andidentifying the document as a document comprising forged informationfrom an approximate match of at least one forger writing profilerepresentation with writing in the document.
 583. The system of claim582, wherein at least one forger writing profile representation is amember of a forger writing profile associated with a known forger. 584.The system of claim 582, wherein at least one forger writing profilerepresentation is a member of a forger writing profile associated with aknown forger, and further comprising identifying the forger of thedocument from the forger writing profile if the document is identifiedas forged.
 585. The system of claim 582, wherein at least one forgerwriting profile representation comprises a forger identity and writingof the forger, and further comprising identifying a forger of thedocument from the forger writing profile.
 586. The system of claim 582,wherein at least one forger writing profile representation is a memberof a forger writing profile associated with a known forger, and furthercomprising identifying the forger of the document from the forgerwriting profile if the document is identified as forged.
 587. A carriermedium comprising program instructions, wherein the program instructionsare computer-executable to implement a method for identifying a documentcomprising forged information, the method comprising: providing thedocument to the computer system, wherein the document comprises at leasttwo information fields; comparing writing in at least two of theinformation fields of the document to at least one forger writingprofile representation from at least one information field of at leastone document comprising forged information; and identifying the documentas a document comprising forged information from an approximate match ofat least one forger writing profile representation with writing in thedocument.
 588. The carrier medium of claim 587, wherein at least oneforger writing profile representation is a member of a forger writingprofile associated with a known forger.
 589. The carrier medium of claim587, wherein at least one forger writing profile representation is amember of a forger writing profile associated with a known forger, andfurther comprising identifying the forger of the document from theforger handwriting profile if the document is identified as forged. 590.The carrier medium of claim 587, wherein at least one forger handwritingprofile representation comprises a forger identity and writing of theforger, and further comprising identifying a forger of the document fromthe forger writing profile.
 591. The carrier medium of claim 587,wherein at least one forger writing profile representation is a memberof a forger writing profile associated with a known forger, and furthercomprising identifying the forger of the document from the forgerwriting profile if the document is identified as forged.
 592. A methodof identifying a document comprising forged information using a computersystem, comprising: providing the document to the computer system,wherein the document comprises at least one information field; comparingwriting in at least one of the information fields of the document to atleast one forger writing profile representation from at least twoinformation fields of at least one document comprising forgedinformation; and identifying the document as a document comprisingforged information from an approximate match of at least one forgerwriting profile representation with writing in the document. 593-628.(cancelled).
 629. A system, comprising: a CPU; a data memory coupled tothe CPU; and a system memory coupled to the CPU, wherein the systemmemory is configured to store one or more computer programs executableby the CPU, and wherein the computer programs are executable toimplement a method for identifying a document comprising forgedinformation, the method comprising: providing the document to thecomputer system, wherein the document comprises at least two informationfields; comparing writing in at least two of the information fields ofthe document to at least one forger writing profile representation fromat least one information field of at least one document comprisingforged information; and identifying the document as a documentcomprising forged information from an approximate match of at least oneforger writing profile representation with writing in the document.630-633. (cancelled).
 634. A carrier medium comprising programinstructions, wherein the program instructions are computer-executableto implement a method for identifying a document comprising forgedinformation, the method comprising: providing the document to thecomputer system, wherein the document comprises at least two informationfields; comparing writing in at least two of the information fields ofthe document to at least one forger writing profile representation fromat least one information field of at least one document comprisingforged information; and identifying the document as a documentcomprising forged information from an approximate match of at least oneforger writing profile representation with writing in the document.635-638. (cancelled).
 639. A method of identifying a document comprisingforged information using a computer system, comprising: providing thedocument to the computer system, wherein the document comprises at leastone information field; comparing writing in at least one of theinformation fields of the document to at least two forger writingprofile representations from at least one information field of at leastone document comprising forged information; and identifying the documentas a document comprising forged information from an approximate match ofat least one forger writing profile representation with writing in thedocument. 640-675. (cancelled).
 676. A system, comprising: a CPU; a datamemory coupled to the CPU; and a system memory coupled to the CPU,wherein the system memory is configured to store one or more computerprograms executable by the CPU, and wherein the computer programs areexecutable to implement a method for identifying a document comprisingforged information, the method comprising: providing the document to thecomputer system, wherein the document comprises at least two informationfields; comparing writing in at least two of the information fields ofthe document to at least one forger writing profile representation fromat least one information field of at least one document comprisingforged information; and identifying the document as a documentcomprising forged information from an approximate match of at least oneforger writing profile representation with writing in the document.677-680. (cancelled).
 681. A carrier medium comprising programinstructions, wherein the program instructions are computer-executableto implement a method for identifying a document comprising forgedinformation, the method comprising: providing the document to thecomputer system, wherein the document comprises at least two informationfields; comparing writing in at least two of the information fields ofthe document to at least one forger writing profile representation fromat least one information field of at least one document comprisingforged information; and identifying the document as a documentcomprising forged information from an approximate match of at least oneforger writing profile representation with writing in the document.682-1092. (cancelled)